Deep learning-based radiomics: pacing immunotherapy in lung cancer
Authors: Nicola Sverzellati, Cristina Marrocchio
Journal: The Lancet Digital Health (2023)

Immunotherapy has substantially changed the therapeutic strategies for many cancers, including lung tumours.1Thai AA Solomon BJ Sequist LV Gainor JF Heist RS Lung cancer.Lancet. 2021; 398: 535-554Summary Full Text Full Text PDF PubMed Scopus (454) Google Scholar However, a durable response to treatment is observed only in a subset of patients (20–30%), and the main biomarkers available, such as PD-L1 and tumour mutational burden, are imperfect. Therefore, the identification of novel predictors of response represents an important and still unmet research need.1Thai AA Solomon BJ Sequist LV Gainor JF Heist RS Lung cancer.Lancet. 2021; 398: 535-554Summary Full Text Full Text PDF PubMed Scopus (454) Google Scholar, 2Ribas A Wolchok JD Cancer immunotherapy using checkpoint blockade.Science. 2018; 359: 1350-1355Crossref PubMed Scopus (3204) Google Scholar In their retrospective study published in The Lancet Digital Health, Maliazurina Saad and colleagues3Saad MB Hong L Aminu M et al.Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study.Lancet Digit Health. 2023; (published online May 31.)https://doi.org/10.1016/S2589-7500(23)00082-1Google Scholar developed a CT-based deep learning signature to predict survival in 976 patients with metastatic, EGFR/ALK wild-type non-small-cell lung cancer (NSCLC) treated with immunotherapy. The obtained model was able to group patients into high-risk and low-risk cohorts for overall survival and progression-free survival and was still predictive when the population was stratified according to relevant clinicopathological factors, including PD-L1 expression, histology, age, gender, and race. Of note, the highest predictive performance was obtained with a model integrating the imaging-based deep learning signature with a benchmark clinical model, which also outperformed PD-L1 tumour proportion score and disease burden. Radiological images represent a potential source of easily accessible, non-invasive, relatively low-cost, and longitudinal information in patients with cancer. Saad and colleagues' analysis suggests deep learning might be able to identify new disease phenotypes related to prognosis relying on measurable, objective data and other non-visually assessable imaging features. However, one of the main obstacles to the introduction of imaging-based models in clinical practice is the drop in the performance of developed algorithms when tested on external datasets.4Yu AC Mohajer B Eng J External validation of deep learning algorithms for radiologic diagnosis: a systematic review.Radiol Artif Intell. 2022; 4e210064Crossref PubMed Scopus (25) Google Scholar In their study, Saad and colleagues used an ensemble learning strategy to obtain a deep CT signature from four different three-dimensional convolutional neural network models, combining supervised, unsupervised, and hybrid learning networks. The authors showed that the obtained ensemble model had a superior performance to each deep learning subnetwork, and that all four subnetworks were necessary to obtain the best prediction performance possible.3Saad MB Hong L Aminu M et al.Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study.Lancet Digit Health. 2023; (published online May 31.)https://doi.org/10.1016/S2589-7500(23)00082-1Google Scholar Although an ensemble model requires a more elaborate architecture, the resulting CT signature appeared to be robust. Of note, it was able to stratify a heterogeneous population imaged in different hospitals with variable CT protocols. The deep CT signature maintained its predictive value even after the stratification of patients according to the imaging modality, which included unenhanced CT, contrast-enhanced CT, and PET–CT.3Saad MB Hong L Aminu M et al.Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study.Lancet Digit Health. 2023; (published online May 31.)https://doi.org/10.1016/S2589-7500(23)00082-1Google Scholar The variability of CT protocols represents a common issue for artificial intelligence (AI) models, which can be greatly affected by variations in technical parameters of image acquisition. However, AI algorithms developed on tailored, ideal protocols might be substantially limited in their general applicability. Until a larger-scale consensus on standardised parameters optimised for these analyses is reached, models that can be applied to real-world data are of utmost importance. The validation of the model by Saad and colleagues on an external, although small, cohort for overall survival is promising for its wider applicability. However, as the authors acknowledged, a more robust validation of the model on a larger, prospective, and more homogeneous population will be essential to confirm its predictivity and specificity for immunotherapy and to identify any existing bias.3Saad MB Hong L Aminu M et al.Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study.Lancet Digit Health. 2023; (published online May 31.)https://doi.org/10.1016/S2589-7500(23)00082-1Google Scholar Another important issue to consider before any AI-based predictive model can be introduced into clinical practice, especially for treatment decisions, is the interpretability of the algorithms. Saad and colleagues correlated their signature with interpretable radiomics features and biological data and used an attention map to show the regions the deep learning signature focused on in the analysis.3Saad MB Hong L Aminu M et al.Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study.Lancet Digit Health. 2023; (published online May 31.)https://doi.org/10.1016/S2589-7500(23)00082-1Google Scholar The attention map can be seen as a visual explanation for deep neural network decisions, resulting in increased model transparency and, consequently, improved interpretability and the possibility to identify potential bias in training datasets.5Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-cam: visual explanations from deep networks via gradient-based localization. 2017 IEEE International Conference on Computer Vision; Oct 22–29: 618–26.Google Scholar The attention map in the study showed that the developed model weighted more on the tumour regions.3Saad MB Hong L Aminu M et al.Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study.Lancet Digit Health. 2023; (published online May 31.)https://doi.org/10.1016/S2589-7500(23)00082-1Google Scholar In their study, Saad and colleagues proposed a CT-based deep learning signature that can predict clinical benefit from immunotherapy in patients with NSCLC, trying to provide a solution to an important clinical need.3Saad MB Hong L Aminu M et al.Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective study.Lancet Digit Health. 2023; (published online May 31.)https://doi.org/10.1016/S2589-7500(23)00082-1Google Scholar Although the model will need further validation and the introduction in clinical practice might be still far ahead, this study is another step towards the development of multidomain models that could improve the prognostication of patients with lung cancer undergoing immunotherapy. We declare no competing interests. Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: a retrospective studyThis proof-of-concept study shows that automated profiling of radiographic scans through deep learning can provide orthogonal information independent of existing clinicopathological biomarkers, bringing the goal of precision immunotherapy for patients with NSCLC closer. Full-Text PDF Open Access